P1266: FLOW CYTOMETRY COMBINED WITH SYSTEMS BIOLOGY ENABLES RATIONAL TARGETING OF NFKB IN DLBCL
Authors: E. Jayawant, A. Pepper, C. Pepper, S. Mitchell
Journal: HemaSphere (2022)

Background: NFκB signalling plays a crucial role in lymphoid malignancies and is frequently mutated leading to aberrant activation. Broad targeting of NFκB has failed due to on-target toxicity, and the roles of specific NFκB molecules in lymphoma remain poorly understood. Previous studies have identified high abundance of NFκB-cRel in GC-like lymphomas, and a subset of DLBCL cases dependent on NFκB-RelB. This suggests that characterisation of NFκB composition and regulatory network state may unlock the ability to rationally target NFκB in lymphoma cells, whilst avoiding systemic toxicity. Systems biology approaches, using computational models, have provided insight into the roles and regulation of specific NFκB components. Capturing the heterogeneous state of NFκB signalling in DLBCL in computational models enables prediction of the efficacy of targeting NFκB in subsets of DLBCL, this drastically reduces laboratory time. Aims: We aimed to develop a library of computational simulations, informed by flow cytometry data from different DLBCL cell lines, capturing the heterogeneous state of NFκB signalling in DLBCL. We aim to establish these models as tools that enable the prediction of the impact of targeted therapeutics on NFκB. Methods: Using a bespoke flow cytometry analysis pipeline, we have collected intracellular flow cytometry data for four NFκB subunits in two DLBCL cell lines. The flow cytometric data were validated using NFκB subunit ELISAs. We further analysed two subclones found within U2932 cells (R1 and R2). These data informed ordinary differential equation (ODE) computational models, allowing us to change parameters encoding expression rates of NFκB subunits to reflect the heterogeneity between DLBCL cell lines (Figure 1A). Results: We validated our flow cytometry pipeline using published data in multiple myeloma cells and characterised the relative abundance of NFκB subunits in DLBCL cell lines. Key differences in subunit abundances, not explained by cell-of-origin classifications, were revealed. SUDHL8 cells showed increased canonical activity compared to U2932 cells. Even within a single cell line (U2932), two subclones had distinct NFκB signalling states, with increased canonical activity in R1. These differences in NFκB signalling state result in distinct predicted response to NFκB targeting therapeutics. Our models were able to recapitulate the vast heterogeneity within these cell lines with only minor changes to expression levels, consistent with epigenetic heterogeneity (Figure 1B). We found NFκB signalling states in DLBCL to be a complex emergent property resulting from competition between monomers, dimers and trimers. We used computational cell lines to predict the impact of targeted therapeutics on NFκB signalling. Image:Summary/Conclusion: NFκB is an exciting target but challenging drug target due to its heterogenous activity in DLBCL cases and importance in normal cellular functions. Systems biology approaches can enhance our understanding of NFκB and the roles of specific subunits in DLBCL. Using a combination of flow cytometry and ODE modelling, we show that a computational model can recapitulate the heterogeneity of the different NFκB subunit abundances observed in DLBCL cell lines and predict the impact of targeted therapies on NFκB. We are now expanding the pipeline from flow cytometry to computational model and back to laboratory experiments, to include patient samples. Long term this work provides a foundation for a personalised medicine approach in which models enable the prediction of which drugs will be most effective for individual DLBCL patients.